An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

Liu, Fei; Li, Xin; Jian, Wang; Zhang, Jixian

doi:10.3390/rs11222628

Cited by 38 publications

(25 citation statements)

References 25 publications

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“…1 can be obtained by substituting ( 6) into (7), and the initial positioning result can be obtained by substituting 1 into (6). Equation ( 4)-( 7) is the process of solving (2) and (3).…”

Section: A Initial Localizationmentioning

confidence: 99%

“…Equation ( 4)-( 7) is the process of solving (2) and (3). The positioning result of the first step can be solved by ( 1)- (7).…”

Section: A Initial Localizationmentioning

confidence: 99%

“…Indoor positioning and navigation, as an essential field of LBS, have received widespread attention. Different indoor positioning technologies have been proposed by researchers around the world, including Wi-Fi [1]- [3], Bluetooth [4]- [6], UWB [7]- [9], Pedestrian Dead Reckoning (PDR) [10]- [12], image [13]- [14], and mobile communication networks [15]- [17]. Among them, indoor positioning technology based on mobile communication network has the following advantages over other positioning technologies: 1. wide coverage 2. a unified standard 3. no need for additional positioning equipment and terminals.…”

Section: Introductionmentioning

confidence: 99%

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A TDOA and PDR Fusion Method for 5G Indoor Localization Based on Virtual Base Stations in Unknown Areas

et al. 2020

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Indoor positioning and navigation is an essential field of location-based service. Non-line of sight (NLOS) error restricts the accuracy of indoor positioning. Many researchers have studied the localization problem in indoor NLOS environments, but there is still a problem that NLOS error cannot be mitigated in unknown areas. To solve the above problems, this paper proposes a method of constructing virtual base stations in unknown areas (UA-VBS), and presents the corresponding positioning algorithm to calculate the location of the user equipment. Firstly, the base stations are selected and the initial positioning is carried out. Then, multiple virtual base stations are constructed according to the user equipment positions in the first three steps. The LOS base stations and virtual base stations participate in the TDOA calculation together, and calculate the base stations' combination with the minimum residual and the corresponding positioning result. Finally, the pedestrian dead reckoning fusion weight is updated by the residual value, and the accurate positioning result in NLOS environment is obtained. Simulation and experimental results show that the proposed algorithm has high positioning accuracy and stability in NLOS environment.

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“…1 can be obtained by substituting ( 6) into (7), and the initial positioning result can be obtained by substituting 1 into (6). Equation ( 4)-( 7) is the process of solving (2) and (3).…”

Section: A Initial Localizationmentioning

confidence: 99%

“…Equation ( 4)-( 7) is the process of solving (2) and (3). The positioning result of the first step can be solved by ( 1)- (7).…”

Section: A Initial Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A TDOA and PDR Fusion Method for 5G Indoor Localization Based on Virtual Base Stations in Unknown Areas

et al. 2020

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show abstract

“…In this area, vision-based indoor positioning of smartphones is an important indoor positioning technology, which does not require much extra consumption to change the indoor environment and only needs to use the existing decorative texture information in a room. Vision-based indoor positioning has the advantages of strong practicality and wide coverage [2][3][4][5][6]; moreover, it is an efficient expansion of positioning technologies based on Bluetooth/iBeacon [7], WIFI [8], UWB [9,10], PDR [11], INS [12], and Geomagnetic Fields [13], with the benefits of better accuracy and lower cost.…”

Section: Introductionmentioning

confidence: 99%

A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras

Chen

Liao

et al. 2020

Remote Sensing

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Indoor visual positioning is a key technology in a variety of indoor location services and applications. The particular spatial structures and environments of indoor spaces is a challenging scene for visual positioning. To address the existing problems of low positioning accuracy and low robustness, this paper proposes a precision single-image-based indoor visual positioning method for a smartphone. The proposed method includes three procedures: First, color sequence images of the indoor environment are collected in an experimental room, from which an indoor precise-positioning-feature database is produced, using a classic speed-up robust features (SURF) point matching strategy and the multi-image spatial forward intersection. Then, the relationships between the smartphone positioning image SURF feature points and object 3D points are obtained by an efficient similarity feature description retrieval method, in which a more reliable and correct matching point pair set is obtained, using a novel matching error elimination technology based on Hough transform voting. Finally, efficient perspective-n-point (EPnP) and bundle adjustment (BA) methods are used to calculate the intrinsic and extrinsic parameters of the positioning image, and the location of the smartphone is obtained as a result. Compared with the ground truth, results of the experiments indicate that the proposed approach can be used for indoor positioning, with an accuracy of approximately 10 cm. In addition, experiments show that the proposed method is more robust and efficient than the baseline method in a real scene. In the case where sufficient indoor textures are present, it has the potential to become a low-cost, precise, and highly available indoor positioning technology.

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“…Horiba has detected the condition of NLOS by the random characteristics of the measurement error and the modified iterative minimum residual method (IMR) [14]. Liu F. has proposed a method that use complementary Kalman filters to integrate UWB and IMU data to improve positioning accuracy [15]. Gao H. has proposed a tightly coupled multi-sensor fusion algorithm to effectively reduce NLOS and multipath interference, a fuzzy calibration is introduced to adaptively adjust the dependency on the received UWB measurement [16].…”

Section: Introductionmentioning

confidence: 99%

Semi-Autonomous Coordinate Configuration Technology of Base Stations in Indoor Positioning System Based on UWB

Yang

Wang

et al. 2019

Electronics

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In the global positioning system (GPS) denied environment, an indoor positioning system based on ultra-wide band (UWB) technology has been utilized for target location and navigation. It can provide a more accurate positioning measurement than those based on received signal strength (RSS). Although promising, it suffers from some shortcomings that base stations should be preinstalled to obtain reference coordinate information, just as navigation satellites in the GPS system. In order to improve the positioning accuracy, a large number of base stations should be preinstalled and assigned coordinates in the large-scale network. However, the coordinate setup process of the base stations is cumbersome, time consuming, and laborious. For a class of linear network topology, a semi-autonomous coordinate configuration technology of base stations is designed, which refers to three conceptions of segmentation, virtual triangle, and bidirectional calculation. It consists of two stages in every segment: Forward and backward. In the forward stage, it utilizes the manual coordinate setup method to deal with the foremost two base stations, and then the remaining base stations autonomously calculate their coordinates by building the virtual triangle train. In the backward stage, the reverse operation is performed, but the foremost two base stations of the next segment should be used as the head. In the last segment, the last two base stations should be used as the head. Integrating forward and backward data, the base stations could improve their location accuracy. It is shown that our algorithm is feasible and practical in simulation results and can dramatically reduce the system configuration time. In addition, the error and maximum base station number for one segment caused by our algorithm are discussed theoretically.

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An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

Cited by 38 publications

References 25 publications

A TDOA and PDR Fusion Method for 5G Indoor Localization Based on Virtual Base Stations in Unknown Areas

A TDOA and PDR Fusion Method for 5G Indoor Localization Based on Virtual Base Stations in Unknown Areas

A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras

Semi-Autonomous Coordinate Configuration Technology of Base Stations in Indoor Positioning System Based on UWB

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